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Active Machine Learning with Python

You're reading from   Active Machine Learning with Python Refine and elevate data quality over quantity with active learning

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835464946
Length 176 pages
Edition 1st Edition
Languages
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Author (1):
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Margaux Masson-Forsythe Margaux Masson-Forsythe
Author Profile Icon Margaux Masson-Forsythe
Margaux Masson-Forsythe
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Table of Contents (13) Chapters Close

Preface 1. Part 1: Fundamentals of Active Machine Learning
2. Chapter 1: Introducing Active Machine Learning FREE CHAPTER 3. Chapter 2: Designing Query Strategy Frameworks 4. Chapter 3: Managing the Human in the Loop 5. Part 2: Active Machine Learning in Practice
6. Chapter 4: Applying Active Learning to Computer Vision 7. Chapter 5: Leveraging Active Learning for Big Data 8. Part 3: Applying Active Machine Learning to Real-World Projects
9. Chapter 6: Evaluating and Enhancing Efficiency 10. Chapter 7: Utilizing Tools and Packages for Active ML 11. Index 12. Other Books You May Enjoy

Using active ML for a segmentation project

In this section, we will reuse what we did for the object detection task, but instead of using an object detection dataset, we will use an instance segmentation dataset with the segment task of yolov8.

Instance segmentation is a computer vision task that involves detecting and segmenting individual objects in an image at the pixel level. It combines elements of object detection, which localizes objects by drawing bounding boxes around them, and semantic segmentation, which classifies each pixel in the image according to the class it belongs to. Instance segmentation goes a step further – it assigns an instance label to each segmented object. The output is a set of masks, one per detected object instance, that indicate the exact pixels that belong to each object. Instance segmentation provides a more detailed delineation of objects compared to the bounding boxes produced in object detection. It segments objects at the pixel level...

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